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cs.CL2024
BabyHGRN: Exploring RNNs for Sample-Efficient Training of Language Models
Patrick Haller, Jonas Golde, Alan Akbik
This paper explores the potential of recurrent neural networks (RNNs) and other subquadratic architectures as competitive alternatives to transformer-based models in low-resource l…
cs.CL2024
Familiarity: Better Evaluation of Zero-Shot Named Entity Recognition by Quantifying Label Shifts in Synthetic Training Data
Jonas Golde, Patrick Haller, Max Ploner +3
Zero-shot named entity recognition (NER) is the task of detecting named entities of specific types (such as 'Person' or 'Medicine') without any training examples. Current research…
cs.CL2024
Large-Scale Label Interpretation Learning for Few-Shot Named Entity Recognition
Jonas Golde, Felix Hamborg, Alan Akbik
Few-shot named entity recognition (NER) detects named entities within text using only a few annotated examples. One promising line of research is to leverage natural language descr…